Tooth Detection and Numbering in Panoramic Radiographs Using YOLOv8-Based Approach
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60665-6_18
Reference33 articles.
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2. Al-Sarem, M., Al-Asali, M., Alqutaibi, A.Y., Saeed, F.: Enhanced tooth region detection using pretrained deep learning models. Int. J. Environ. Res. Public Health 19(22), 15414 (2022)
3. Alam, M.K., et al.: Teeth segmentation by optical radiographic images using VGG-16 deep learning convolution architecture with R-CNN network approach for biomedical sensing applications. Opt. Quant. Electron. 55(9), 808 (2023)
4. Almalki, A., Latecki, L.J.: Self-supervised learning with masked image modeling for teeth numbering, detection of dental restorations, and instance segmentation in dental panoramic radiographs. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 5594–5603 (2023)
5. Astuti, E.R., et al.: The sensitivity and specificity of YOLO v4 for tooth detection on panoramic radiographs. J. Int. Dent. Med. Res. 16(1), 442–446 (2023)
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